Overview

Dataset statistics

Number of variables25
Number of observations8763
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory200.0 B

Variable types

Text5
Numeric8
Categorical12

Alerts

Sex is highly overall correlated with SmokingHigh correlation
Smoking is highly overall correlated with SexHigh correlation
Country is highly overall correlated with ContinentHigh correlation
Continent is highly overall correlated with CountryHigh correlation
Smoking is highly imbalanced (52.1%)Imbalance
Patient-ID has unique valuesUnique
Exercise-Hours-Per-Week has unique valuesUnique
Sedentary-Hours-Per-Day has unique valuesUnique
BMI has unique valuesUnique
Physical-Activity-Days-Per-Week has 1065 (12.2%) zerosZeros

Reproduction

Analysis started2023-10-10 16:18:10.786800
Analysis finished2023-10-10 16:18:38.661777
Duration27.87 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

Patient-ID
Text

UNIQUE 

Distinct8763
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
2023-10-10T16:18:39.210839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters61341
Distinct characters45
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8763 ?
Unique (%)100.0%

Sample

1st rowBMW7812
2nd rowCZE1114
3rd rowBNI9906
4th rowJLN3497
5th rowGFO8847
ValueCountFrequency (%)
bmw7812 1
 
< 0.1%
hsd6283 1
 
< 0.1%
xrl5497 1
 
< 0.1%
dcy3282 1
 
< 0.1%
vtw9069 1
 
< 0.1%
yyu9565 1
 
< 0.1%
fps0415 1
 
< 0.1%
ysp0073 1
 
< 0.1%
ftj5456 1
 
< 0.1%
dxt5853 1
 
< 0.1%
Other values (8753) 8753
99.9%
2023-10-10T16:18:40.441235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 3549
 
5.8%
9 3543
 
5.8%
2 3537
 
5.8%
0 3523
 
5.7%
8 3507
 
5.7%
7 3505
 
5.7%
3 3485
 
5.7%
1 3482
 
5.7%
6 3461
 
5.6%
5 3460
 
5.6%
Other values (35) 26289
42.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35052
57.1%
Uppercase Letter 26271
42.8%
Lowercase Letter 18
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 1084
 
4.1%
O 1051
 
4.0%
X 1047
 
4.0%
T 1046
 
4.0%
R 1045
 
4.0%
K 1044
 
4.0%
U 1037
 
3.9%
I 1036
 
3.9%
B 1028
 
3.9%
Y 1020
 
3.9%
Other values (16) 15833
60.3%
Decimal Number
ValueCountFrequency (%)
4 3549
10.1%
9 3543
10.1%
2 3537
10.1%
0 3523
10.1%
8 3507
10.0%
7 3505
10.0%
3 3485
9.9%
1 3482
9.9%
6 3461
9.9%
5 3460
9.9%
Lowercase Letter
ValueCountFrequency (%)
a 4
22.2%
r 3
16.7%
j 2
11.1%
u 2
11.1%
b 2
11.1%
m 2
11.1%
n 1
 
5.6%
l 1
 
5.6%
y 1
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 35052
57.1%
Latin 26289
42.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 1084
 
4.1%
O 1051
 
4.0%
X 1047
 
4.0%
T 1046
 
4.0%
R 1045
 
4.0%
K 1044
 
4.0%
U 1037
 
3.9%
I 1036
 
3.9%
B 1028
 
3.9%
Y 1020
 
3.9%
Other values (25) 15851
60.3%
Common
ValueCountFrequency (%)
4 3549
10.1%
9 3543
10.1%
2 3537
10.1%
0 3523
10.1%
8 3507
10.0%
7 3505
10.0%
3 3485
9.9%
1 3482
9.9%
6 3461
9.9%
5 3460
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61341
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 3549
 
5.8%
9 3543
 
5.8%
2 3537
 
5.8%
0 3523
 
5.7%
8 3507
 
5.7%
7 3505
 
5.7%
3 3485
 
5.7%
1 3482
 
5.7%
6 3461
 
5.6%
5 3460
 
5.6%
Other values (35) 26289
42.9%

Age
Real number (ℝ)

Distinct73
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.707977
Minimum18
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-10-10T16:18:40.798777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21
Q135
median54
Q372
95-th percentile87
Maximum90
Range72
Interquartile range (IQR)37

Descriptive statistics

Standard deviation21.249509
Coefficient of variation (CV)0.39564903
Kurtosis-1.213755
Mean53.707977
Median Absolute Deviation (MAD)18
Skewness0.028497567
Sum470643
Variance451.54162
MonotonicityNot monotonic
2023-10-10T16:18:41.127813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 152
 
1.7%
42 150
 
1.7%
33 147
 
1.7%
59 147
 
1.7%
29 137
 
1.6%
28 136
 
1.6%
36 135
 
1.5%
54 134
 
1.5%
38 134
 
1.5%
67 133
 
1.5%
Other values (63) 7358
84.0%
ValueCountFrequency (%)
18 123
1.4%
19 128
1.5%
20 130
1.5%
21 117
1.3%
22 124
1.4%
23 108
1.2%
24 130
1.5%
25 132
1.5%
26 112
1.3%
27 125
1.4%
ValueCountFrequency (%)
90 152
1.7%
89 117
1.3%
88 123
1.4%
87 126
1.4%
86 105
1.2%
85 118
1.3%
84 126
1.4%
83 115
1.3%
82 127
1.4%
81 113
1.3%

Sex
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
Masculino
6111 
Femenino
2652 

Length

Max length11
Median length11
Mean length10.394728
Min length9

Characters and Unicode

Total characters91089
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row Masculino
2nd row Masculino
3rd row Femenino
4th row Masculino
5th row Masculino

Common Values

ValueCountFrequency (%)
Masculino 6111
69.7%
Femenino 2652
30.3%

Length

2023-10-10T16:18:41.426021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T16:18:41.734052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
masculino 6111
69.7%
femenino 2652
30.3%

Most occurring characters

ValueCountFrequency (%)
14874
16.3%
n 11415
12.5%
i 8763
9.6%
o 8763
9.6%
M 6111
6.7%
a 6111
6.7%
s 6111
6.7%
c 6111
6.7%
u 6111
6.7%
l 6111
6.7%
Other values (3) 10608
11.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 67452
74.1%
Space Separator 14874
 
16.3%
Uppercase Letter 8763
 
9.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 11415
16.9%
i 8763
13.0%
o 8763
13.0%
a 6111
9.1%
s 6111
9.1%
c 6111
9.1%
u 6111
9.1%
l 6111
9.1%
e 5304
7.9%
m 2652
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
M 6111
69.7%
F 2652
30.3%
Space Separator
ValueCountFrequency (%)
14874
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 76215
83.7%
Common 14874
 
16.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 11415
15.0%
i 8763
11.5%
o 8763
11.5%
M 6111
8.0%
a 6111
8.0%
s 6111
8.0%
c 6111
8.0%
u 6111
8.0%
l 6111
8.0%
e 5304
7.0%
Other values (2) 5304
7.0%
Common
ValueCountFrequency (%)
14874
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14874
16.3%
n 11415
12.5%
i 8763
9.6%
o 8763
9.6%
M 6111
6.7%
a 6111
6.7%
s 6111
6.7%
c 6111
6.7%
u 6111
6.7%
l 6111
6.7%
Other values (3) 10608
11.6%

Cholesterol
Real number (ℝ)

Distinct281
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean259.87721
Minimum120
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-10-10T16:18:41.984256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile133
Q1192
median259
Q3330
95-th percentile385
Maximum400
Range280
Interquartile range (IQR)138

Descriptive statistics

Standard deviation80.863276
Coefficient of variation (CV)0.31115955
Kurtosis-1.1802463
Mean259.87721
Median Absolute Deviation (MAD)69
Skewness-0.00095473969
Sum2277304
Variance6538.8694
MonotonicityNot monotonic
2023-10-10T16:18:42.252492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
235 52
 
0.6%
360 47
 
0.5%
149 46
 
0.5%
218 46
 
0.5%
251 45
 
0.5%
254 44
 
0.5%
185 43
 
0.5%
370 43
 
0.5%
294 43
 
0.5%
206 42
 
0.5%
Other values (271) 8312
94.9%
ValueCountFrequency (%)
120 32
0.4%
121 33
0.4%
122 31
0.4%
123 31
0.4%
124 34
0.4%
125 39
0.4%
126 34
0.4%
127 36
0.4%
128 32
0.4%
129 37
0.4%
ValueCountFrequency (%)
400 34
0.4%
399 34
0.4%
398 20
0.2%
397 19
0.2%
396 32
0.4%
395 33
0.4%
394 30
0.3%
393 25
0.3%
392 36
0.4%
391 24
0.3%
Distinct3915
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
2023-10-10T16:18:42.780522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1090951
Min length5

Characters and Unicode

Total characters53534
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1297 ?
Unique (%)14.8%

Sample

1st row158/88
2nd row165/93
3rd row174/99
4th row163/100
5th row91/88
ValueCountFrequency (%)
146/94 8
 
0.1%
101/93 8
 
0.1%
106/64 7
 
0.1%
176/77 7
 
0.1%
139/106 7
 
0.1%
145/104 7
 
0.1%
140/95 7
 
0.1%
129/106 7
 
0.1%
94/109 7
 
0.1%
147/94 7
 
0.1%
Other values (3905) 8691
99.2%
2023-10-10T16:18:43.665525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12525
23.4%
/ 8763
16.4%
0 4737
 
8.8%
6 4441
 
8.3%
9 4411
 
8.2%
7 4365
 
8.2%
8 3533
 
6.6%
3 2721
 
5.1%
4 2700
 
5.0%
2 2691
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44771
83.6%
Other Punctuation 8763
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12525
28.0%
0 4737
 
10.6%
6 4441
 
9.9%
9 4411
 
9.9%
7 4365
 
9.7%
8 3533
 
7.9%
3 2721
 
6.1%
4 2700
 
6.0%
2 2691
 
6.0%
5 2647
 
5.9%
Other Punctuation
ValueCountFrequency (%)
/ 8763
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53534
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12525
23.4%
/ 8763
16.4%
0 4737
 
8.8%
6 4441
 
8.3%
9 4411
 
8.2%
7 4365
 
8.2%
8 3533
 
6.6%
3 2721
 
5.1%
4 2700
 
5.0%
2 2691
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12525
23.4%
/ 8763
16.4%
0 4737
 
8.8%
6 4441
 
8.3%
9 4411
 
8.2%
7 4365
 
8.2%
8 3533
 
6.6%
3 2721
 
5.1%
4 2700
 
5.0%
2 2691
 
5.0%

Heart-Rate
Real number (ℝ)

Distinct71
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.021682
Minimum40
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-10-10T16:18:44.020386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile43
Q157
median75
Q393
95-th percentile107
Maximum110
Range70
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.550948
Coefficient of variation (CV)0.27393345
Kurtosis-1.2111804
Mean75.021682
Median Absolute Deviation (MAD)18
Skewness-0.0032271859
Sum657415
Variance422.34146
MonotonicityNot monotonic
2023-10-10T16:18:44.322592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 157
 
1.8%
97 146
 
1.7%
57 143
 
1.6%
52 140
 
1.6%
104 139
 
1.6%
105 138
 
1.6%
72 138
 
1.6%
54 137
 
1.6%
87 137
 
1.6%
84 136
 
1.6%
Other values (61) 7352
83.9%
ValueCountFrequency (%)
40 114
1.3%
41 136
1.6%
42 129
1.5%
43 111
1.3%
44 130
1.5%
45 134
1.5%
46 125
1.4%
47 114
1.3%
48 107
1.2%
49 133
1.5%
ValueCountFrequency (%)
110 126
1.4%
109 130
1.5%
108 122
1.4%
107 118
1.3%
106 111
1.3%
105 138
1.6%
104 139
1.6%
103 114
1.3%
102 128
1.5%
101 108
1.2%

Diabetes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
Positivo
5716 
Negativo
3047 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters87630
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row Negativo
2nd row Positivo
3rd row Positivo
4th row Positivo
5th row Positivo

Common Values

ValueCountFrequency (%)
Positivo 5716
65.2%
Negativo 3047
34.8%

Length

2023-10-10T16:18:44.593509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T16:18:44.852094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
positivo 5716
65.2%
negativo 3047
34.8%

Most occurring characters

ValueCountFrequency (%)
17526
20.0%
o 14479
16.5%
i 14479
16.5%
t 8763
10.0%
v 8763
10.0%
P 5716
 
6.5%
s 5716
 
6.5%
N 3047
 
3.5%
e 3047
 
3.5%
g 3047
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 61341
70.0%
Space Separator 17526
 
20.0%
Uppercase Letter 8763
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 14479
23.6%
i 14479
23.6%
t 8763
14.3%
v 8763
14.3%
s 5716
 
9.3%
e 3047
 
5.0%
g 3047
 
5.0%
a 3047
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
P 5716
65.2%
N 3047
34.8%
Space Separator
ValueCountFrequency (%)
17526
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70104
80.0%
Common 17526
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 14479
20.7%
i 14479
20.7%
t 8763
12.5%
v 8763
12.5%
P 5716
 
8.2%
s 5716
 
8.2%
N 3047
 
4.3%
e 3047
 
4.3%
g 3047
 
4.3%
a 3047
 
4.3%
Common
ValueCountFrequency (%)
17526
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17526
20.0%
o 14479
16.5%
i 14479
16.5%
t 8763
10.0%
v 8763
10.0%
P 5716
 
6.5%
s 5716
 
6.5%
N 3047
 
3.5%
e 3047
 
3.5%
g 3047
 
3.5%

Family-History
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
Negativo
4443 
Positivo
4320 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters87630
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row Negativo
2nd row Positivo
3rd row Negativo
4th row Positivo
5th row Positivo

Common Values

ValueCountFrequency (%)
Negativo 4443
50.7%
Positivo 4320
49.3%

Length

2023-10-10T16:18:45.093886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T16:18:45.329719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
negativo 4443
50.7%
positivo 4320
49.3%

Most occurring characters

ValueCountFrequency (%)
17526
20.0%
i 13083
14.9%
o 13083
14.9%
t 8763
10.0%
v 8763
10.0%
N 4443
 
5.1%
e 4443
 
5.1%
g 4443
 
5.1%
a 4443
 
5.1%
P 4320
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 61341
70.0%
Space Separator 17526
 
20.0%
Uppercase Letter 8763
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 13083
21.3%
o 13083
21.3%
t 8763
14.3%
v 8763
14.3%
e 4443
 
7.2%
g 4443
 
7.2%
a 4443
 
7.2%
s 4320
 
7.0%
Uppercase Letter
ValueCountFrequency (%)
N 4443
50.7%
P 4320
49.3%
Space Separator
ValueCountFrequency (%)
17526
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70104
80.0%
Common 17526
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 13083
18.7%
o 13083
18.7%
t 8763
12.5%
v 8763
12.5%
N 4443
 
6.3%
e 4443
 
6.3%
g 4443
 
6.3%
a 4443
 
6.3%
P 4320
 
6.2%
s 4320
 
6.2%
Common
ValueCountFrequency (%)
17526
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17526
20.0%
i 13083
14.9%
o 13083
14.9%
t 8763
10.0%
v 8763
10.0%
N 4443
 
5.1%
e 4443
 
5.1%
g 4443
 
5.1%
a 4443
 
5.1%
P 4320
 
4.9%

Smoking
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
Si
7859 
No
904 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters35052
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row Si
2nd row Si
3rd row No
4th row Si
5th row Si

Common Values

ValueCountFrequency (%)
Si 7859
89.7%
No 904
 
10.3%

Length

2023-10-10T16:18:45.530464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T16:18:45.780881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
si 7859
89.7%
no 904
 
10.3%

Most occurring characters

ValueCountFrequency (%)
17526
50.0%
S 7859
22.4%
i 7859
22.4%
N 904
 
2.6%
o 904
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Space Separator 17526
50.0%
Uppercase Letter 8763
25.0%
Lowercase Letter 8763
25.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 7859
89.7%
N 904
 
10.3%
Lowercase Letter
ValueCountFrequency (%)
i 7859
89.7%
o 904
 
10.3%
Space Separator
ValueCountFrequency (%)
17526
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17526
50.0%
Latin 17526
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 7859
44.8%
i 7859
44.8%
N 904
 
5.2%
o 904
 
5.2%
Common
ValueCountFrequency (%)
17526
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17526
50.0%
S 7859
22.4%
i 7859
22.4%
N 904
 
2.6%
o 904
 
2.6%

Obesity
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
Si
4394 
No
4369 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters35052
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row No
2nd row Si
3rd row No
4th row No
5th row Si

Common Values

ValueCountFrequency (%)
Si 4394
50.1%
No 4369
49.9%

Length

2023-10-10T16:18:46.006470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T16:18:46.248574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
si 4394
50.1%
no 4369
49.9%

Most occurring characters

ValueCountFrequency (%)
17526
50.0%
S 4394
 
12.5%
i 4394
 
12.5%
N 4369
 
12.5%
o 4369
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator 17526
50.0%
Uppercase Letter 8763
25.0%
Lowercase Letter 8763
25.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 4394
50.1%
N 4369
49.9%
Lowercase Letter
ValueCountFrequency (%)
i 4394
50.1%
o 4369
49.9%
Space Separator
ValueCountFrequency (%)
17526
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17526
50.0%
Latin 17526
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 4394
25.1%
i 4394
25.1%
N 4369
24.9%
o 4369
24.9%
Common
ValueCountFrequency (%)
17526
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17526
50.0%
S 4394
 
12.5%
i 4394
 
12.5%
N 4369
 
12.5%
o 4369
 
12.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
Si
5241 
No
3522 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters35052
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row No
2nd row Si
3rd row No
4th row Si
5th row No

Common Values

ValueCountFrequency (%)
Si 5241
59.8%
No 3522
40.2%

Length

2023-10-10T16:18:46.452517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T16:18:46.688643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
si 5241
59.8%
no 3522
40.2%

Most occurring characters

ValueCountFrequency (%)
17526
50.0%
S 5241
 
15.0%
i 5241
 
15.0%
N 3522
 
10.0%
o 3522
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator 17526
50.0%
Uppercase Letter 8763
25.0%
Lowercase Letter 8763
25.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 5241
59.8%
N 3522
40.2%
Lowercase Letter
ValueCountFrequency (%)
i 5241
59.8%
o 3522
40.2%
Space Separator
ValueCountFrequency (%)
17526
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17526
50.0%
Latin 17526
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 5241
29.9%
i 5241
29.9%
N 3522
20.1%
o 3522
20.1%
Common
ValueCountFrequency (%)
17526
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17526
50.0%
S 5241
 
15.0%
i 5241
 
15.0%
N 3522
 
10.0%
o 3522
 
10.0%
Distinct8763
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
2023-10-10T16:18:47.036603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length22
Median length22
Mean length21.391076
Min length17

Characters and Unicode

Total characters187450
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8763 ?
Unique (%)100.0%

Sample

1st row4.168.188.835.442.070
2nd row18.132.416.178.634.400
3rd row20.783.529.861.178.800
4th row982.812.959.348.533
5th row5.804.298.820.315.430
ValueCountFrequency (%)
4.168.188.835.442.070 1
 
< 0.1%
16.841.987.593.616.600 1
 
< 0.1%
3.466.864.143.947.520 1
 
< 0.1%
1.455.966.449.587.790 1
 
< 0.1%
15.387.604.628.162.700 1
 
< 0.1%
17.037.374.183.793.800 1
 
< 0.1%
19.633.268.156.072.200 1
 
< 0.1%
8.251.995.072.165.770 1
 
< 0.1%
1.945.150.606.299.490 1
 
< 0.1%
11.395.079.808.991.800 1
 
< 0.1%
Other values (8753) 8753
99.9%
2023-10-10T16:18:47.697587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 43336
23.1%
0 25085
13.4%
1 17129
 
9.1%
6 12872
 
6.9%
2 12810
 
6.8%
3 12776
 
6.8%
9 12765
 
6.8%
4 12720
 
6.8%
5 12709
 
6.8%
8 12640
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 144114
76.9%
Other Punctuation 43336
 
23.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25085
17.4%
1 17129
11.9%
6 12872
8.9%
2 12810
8.9%
3 12776
8.9%
9 12765
8.9%
4 12720
8.8%
5 12709
8.8%
8 12640
8.8%
7 12608
8.7%
Other Punctuation
ValueCountFrequency (%)
. 43336
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 187450
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 43336
23.1%
0 25085
13.4%
1 17129
 
9.1%
6 12872
 
6.9%
2 12810
 
6.8%
3 12776
 
6.8%
9 12765
 
6.8%
4 12720
 
6.8%
5 12709
 
6.8%
8 12640
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 187450
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 43336
23.1%
0 25085
13.4%
1 17129
 
9.1%
6 12872
 
6.9%
2 12810
 
6.8%
3 12776
 
6.8%
9 12765
 
6.8%
4 12720
 
6.8%
5 12709
 
6.8%
8 12640
 
6.7%

Diet
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
Sano
2960 
Promedio
2912 
Enfermizo
2891 

Length

Max length10
Median length10
Mean length8.3110807
Min length5

Characters and Unicode

Total characters72830
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row Promedio
2nd row Enfermizo
3rd row Sano
4th row Promedio
5th row Enfermizo

Common Values

ValueCountFrequency (%)
Sano 2960
33.8%
Promedio 2912
33.2%
Enfermizo 2891
33.0%

Length

2023-10-10T16:18:48.032400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T16:18:48.623346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sano 2960
33.8%
promedio 2912
33.2%
enfermizo 2891
33.0%

Most occurring characters

ValueCountFrequency (%)
11675
16.0%
o 11675
16.0%
n 5851
8.0%
r 5803
8.0%
m 5803
8.0%
e 5803
8.0%
i 5803
8.0%
S 2960
 
4.1%
a 2960
 
4.1%
P 2912
 
4.0%
Other values (4) 11585
15.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 52392
71.9%
Space Separator 11675
 
16.0%
Uppercase Letter 8763
 
12.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 11675
22.3%
n 5851
11.2%
r 5803
11.1%
m 5803
11.1%
e 5803
11.1%
i 5803
11.1%
a 2960
 
5.6%
d 2912
 
5.6%
f 2891
 
5.5%
z 2891
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
S 2960
33.8%
P 2912
33.2%
E 2891
33.0%
Space Separator
ValueCountFrequency (%)
11675
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61155
84.0%
Common 11675
 
16.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 11675
19.1%
n 5851
9.6%
r 5803
9.5%
m 5803
9.5%
e 5803
9.5%
i 5803
9.5%
S 2960
 
4.8%
a 2960
 
4.8%
P 2912
 
4.8%
d 2912
 
4.8%
Other values (3) 8673
14.2%
Common
ValueCountFrequency (%)
11675
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11675
16.0%
o 11675
16.0%
n 5851
8.0%
r 5803
8.0%
m 5803
8.0%
e 5803
8.0%
i 5803
8.0%
S 2960
 
4.1%
a 2960
 
4.1%
P 2912
 
4.0%
Other values (4) 11585
15.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
Negativo
4418 
Positivo
4345 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters87630
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row Negativo
2nd row Positivo
3rd row Positivo
4th row Positivo
5th row Positivo

Common Values

ValueCountFrequency (%)
Negativo 4418
50.4%
Positivo 4345
49.6%

Length

2023-10-10T16:18:48.878460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T16:18:49.174484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
negativo 4418
50.4%
positivo 4345
49.6%

Most occurring characters

ValueCountFrequency (%)
17526
20.0%
i 13108
15.0%
o 13108
15.0%
t 8763
10.0%
v 8763
10.0%
N 4418
 
5.0%
e 4418
 
5.0%
g 4418
 
5.0%
a 4418
 
5.0%
P 4345
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 61341
70.0%
Space Separator 17526
 
20.0%
Uppercase Letter 8763
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 13108
21.4%
o 13108
21.4%
t 8763
14.3%
v 8763
14.3%
e 4418
 
7.2%
g 4418
 
7.2%
a 4418
 
7.2%
s 4345
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
N 4418
50.4%
P 4345
49.6%
Space Separator
ValueCountFrequency (%)
17526
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70104
80.0%
Common 17526
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 13108
18.7%
o 13108
18.7%
t 8763
12.5%
v 8763
12.5%
N 4418
 
6.3%
e 4418
 
6.3%
g 4418
 
6.3%
a 4418
 
6.3%
P 4345
 
6.2%
s 4345
 
6.2%
Common
ValueCountFrequency (%)
17526
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17526
20.0%
i 13108
15.0%
o 13108
15.0%
t 8763
10.0%
v 8763
10.0%
N 4418
 
5.0%
e 4418
 
5.0%
g 4418
 
5.0%
a 4418
 
5.0%
P 4345
 
5.0%

Medication-Use
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
No
4396 
Si
4367 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters35052
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row No
2nd row No
3rd row Si
4th row No
5th row No

Common Values

ValueCountFrequency (%)
No 4396
50.2%
Si 4367
49.8%

Length

2023-10-10T16:18:49.381849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T16:18:49.628077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 4396
50.2%
si 4367
49.8%

Most occurring characters

ValueCountFrequency (%)
17526
50.0%
N 4396
 
12.5%
o 4396
 
12.5%
S 4367
 
12.5%
i 4367
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator 17526
50.0%
Uppercase Letter 8763
25.0%
Lowercase Letter 8763
25.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 4396
50.2%
S 4367
49.8%
Lowercase Letter
ValueCountFrequency (%)
o 4396
50.2%
i 4367
49.8%
Space Separator
ValueCountFrequency (%)
17526
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17526
50.0%
Latin 17526
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 4396
25.1%
o 4396
25.1%
S 4367
24.9%
i 4367
24.9%
Common
ValueCountFrequency (%)
17526
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17526
50.0%
N 4396
 
12.5%
o 4396
 
12.5%
S 4367
 
12.5%
i 4367
 
12.5%

Stress-Level
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4697022
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-10-10T16:18:49.840979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8596219
Coefficient of variation (CV)0.52281126
Kurtosis-1.2254387
Mean5.4697022
Median Absolute Deviation (MAD)2
Skewness0.0083889581
Sum47931
Variance8.1774373
MonotonicityNot monotonic
2023-10-10T16:18:50.042794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 913
10.4%
4 910
10.4%
7 903
10.3%
9 887
10.1%
8 879
10.0%
3 868
9.9%
1 865
9.9%
5 860
9.8%
6 855
9.8%
10 823
9.4%
ValueCountFrequency (%)
1 865
9.9%
2 913
10.4%
3 868
9.9%
4 910
10.4%
5 860
9.8%
6 855
9.8%
7 903
10.3%
8 879
10.0%
9 887
10.1%
10 823
9.4%
ValueCountFrequency (%)
10 823
9.4%
9 887
10.1%
8 879
10.0%
7 903
10.3%
6 855
9.8%
5 860
9.8%
4 910
10.4%
3 868
9.9%
2 913
10.4%
1 865
9.9%
Distinct8763
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
2023-10-10T16:18:50.464488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length22
Median length21
Mean length21.201073
Min length15

Characters and Unicode

Total characters185785
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8763 ?
Unique (%)100.0%

Sample

1st row66.150.014.529.140.500
2nd row4.963.458.839.757.670
3rd row9.463.425.838.029.820
4th row7.648.980.824.461.000
5th row15.148.209.264.291.300
ValueCountFrequency (%)
66.150.014.529.140.500 1
 
< 0.1%
8.919.879.240.644.140 1
 
< 0.1%
18.660.311.482.160.000 1
 
< 0.1%
10.086.478.972.289.000 1
 
< 0.1%
10.425.489.575.178.800 1
 
< 0.1%
8.727.417.211.573.480 1
 
< 0.1%
1.091.752.425.375.180 1
 
< 0.1%
7.227.338.314.321.000 1
 
< 0.1%
4.055.114.781.794.600 1
 
< 0.1%
8.726.360.093.346.490 1
 
< 0.1%
Other values (8753) 8753
99.9%
2023-10-10T16:18:51.454169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 43145
23.2%
0 24174
13.0%
1 15116
 
8.1%
2 13046
 
7.0%
5 13033
 
7.0%
9 12981
 
7.0%
3 12964
 
7.0%
6 12892
 
6.9%
4 12820
 
6.9%
8 12816
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 142640
76.8%
Other Punctuation 43145
 
23.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24174
16.9%
1 15116
10.6%
2 13046
9.1%
5 13033
9.1%
9 12981
9.1%
3 12964
9.1%
6 12892
9.0%
4 12820
9.0%
8 12816
9.0%
7 12798
9.0%
Other Punctuation
ValueCountFrequency (%)
. 43145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 185785
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 43145
23.2%
0 24174
13.0%
1 15116
 
8.1%
2 13046
 
7.0%
5 13033
 
7.0%
9 12981
 
7.0%
3 12964
 
7.0%
6 12892
 
6.9%
4 12820
 
6.9%
8 12816
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 185785
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 43145
23.2%
0 24174
13.0%
1 15116
 
8.1%
2 13046
 
7.0%
5 13033
 
7.0%
9 12981
 
7.0%
3 12964
 
7.0%
6 12892
 
6.9%
4 12820
 
6.9%
8 12816
 
6.9%

Income
Real number (ℝ)

Distinct8615
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158263.18
Minimum20062
Maximum299954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-10-10T16:18:52.015846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20062
5-th percentile32900.5
Q188310
median157866
Q3227749
95-th percentile285578.6
Maximum299954
Range279892
Interquartile range (IQR)139439

Descriptive statistics

Standard deviation80575.191
Coefficient of variation (CV)0.50912151
Kurtosis-1.181923
Mean158263.18
Median Absolute Deviation (MAD)69784
Skewness0.02179166
Sum1.3868603 × 109
Variance6.4923614 × 109
MonotonicityNot monotonic
2023-10-10T16:18:52.509823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225278 4
 
< 0.1%
194461 3
 
< 0.1%
195282 3
 
< 0.1%
220507 2
 
< 0.1%
139451 2
 
< 0.1%
59437 2
 
< 0.1%
133177 2
 
< 0.1%
239754 2
 
< 0.1%
172040 2
 
< 0.1%
179068 2
 
< 0.1%
Other values (8605) 8739
99.7%
ValueCountFrequency (%)
20062 1
< 0.1%
20140 1
< 0.1%
20162 1
< 0.1%
20165 1
< 0.1%
20208 1
< 0.1%
20249 1
< 0.1%
20255 1
< 0.1%
20264 1
< 0.1%
20285 1
< 0.1%
20328 1
< 0.1%
ValueCountFrequency (%)
299954 1
< 0.1%
299909 1
< 0.1%
299891 1
< 0.1%
299850 1
< 0.1%
299810 1
< 0.1%
299776 1
< 0.1%
299771 1
< 0.1%
299769 1
< 0.1%
299742 1
< 0.1%
299738 1
< 0.1%

BMI
Text

UNIQUE 

Distinct8763
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
2023-10-10T16:18:53.108906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length22
Median length22
Mean length21.406596
Min length15

Characters and Unicode

Total characters187586
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8763 ?
Unique (%)100.0%

Sample

1st row31.251.232.725.295.400
2nd row271.949.733.519.874
3rd row28.176.570.683.909.800
4th row36.464.704.293.082.800
5th row21.809.144.180.619.700
ValueCountFrequency (%)
31.251.232.725.295.400 1
 
< 0.1%
22.867.910.772.851.200 1
 
< 0.1%
1.879.283.400.542.970 1
 
< 0.1%
3.652.439.523.724.410 1
 
< 0.1%
25.491.740.663.098.100 1
 
< 0.1%
25.564.897.154.053.200 1
 
< 0.1%
3.510.223.615.396.750 1
 
< 0.1%
3.248.534.524.081.800 1
 
< 0.1%
2.255.891.675.229.810 1
 
< 0.1%
22.040.472.919.767.900 1
 
< 0.1%
Other values (8753) 8753
99.9%
2023-10-10T16:18:54.248475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 43371
23.1%
0 25006
13.3%
2 16272
 
8.7%
3 16215
 
8.6%
1 13129
 
7.0%
9 12516
 
6.7%
8 12499
 
6.7%
6 12321
 
6.6%
5 12204
 
6.5%
4 12115
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 144215
76.9%
Other Punctuation 43371
 
23.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25006
17.3%
2 16272
11.3%
3 16215
11.2%
1 13129
9.1%
9 12516
8.7%
8 12499
8.7%
6 12321
8.5%
5 12204
8.5%
4 12115
8.4%
7 11938
8.3%
Other Punctuation
ValueCountFrequency (%)
. 43371
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 187586
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 43371
23.1%
0 25006
13.3%
2 16272
 
8.7%
3 16215
 
8.6%
1 13129
 
7.0%
9 12516
 
6.7%
8 12499
 
6.7%
6 12321
 
6.6%
5 12204
 
6.5%
4 12115
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 187586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 43371
23.1%
0 25006
13.3%
2 16272
 
8.7%
3 16215
 
8.6%
1 13129
 
7.0%
9 12516
 
6.7%
8 12499
 
6.7%
6 12321
 
6.6%
5 12204
 
6.5%
4 12115
 
6.5%

Triglycerides
Real number (ℝ)

Distinct771
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean417.67705
Minimum30
Maximum800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-10-10T16:18:54.719517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile68
Q1225.5
median417
Q3612
95-th percentile766
Maximum800
Range770
Interquartile range (IQR)386.5

Descriptive statistics

Standard deviation223.74814
Coefficient of variation (CV)0.53569651
Kurtosis-1.1977997
Mean417.67705
Median Absolute Deviation (MAD)193
Skewness-0.0019149561
Sum3660104
Variance50063.229
MonotonicityNot monotonic
2023-10-10T16:18:55.012663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
799 25
 
0.3%
507 22
 
0.3%
121 22
 
0.3%
593 22
 
0.3%
469 22
 
0.3%
731 21
 
0.2%
675 20
 
0.2%
726 20
 
0.2%
352 20
 
0.2%
410 20
 
0.2%
Other values (761) 8549
97.6%
ValueCountFrequency (%)
30 16
0.2%
31 14
0.2%
32 11
0.1%
33 13
0.1%
34 6
 
0.1%
35 13
0.1%
36 7
0.1%
37 10
0.1%
38 13
0.1%
39 7
0.1%
ValueCountFrequency (%)
800 8
 
0.1%
799 25
0.3%
798 12
0.1%
797 19
0.2%
796 13
0.1%
795 9
 
0.1%
794 9
 
0.1%
793 9
 
0.1%
792 9
 
0.1%
791 20
0.2%

Physical-Activity-Days-Per-Week
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4896725
Minimum0
Maximum7
Zeros1065
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-10-10T16:18:55.269412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2826873
Coefficient of variation (CV)0.65412652
Kurtosis-1.2295521
Mean3.4896725
Median Absolute Deviation (MAD)2
Skewness0.017821706
Sum30580
Variance5.2106614
MonotonicityNot monotonic
2023-10-10T16:18:55.497807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 1143
13.0%
1 1121
12.8%
2 1109
12.7%
7 1095
12.5%
5 1079
12.3%
4 1077
12.3%
6 1074
12.3%
0 1065
12.2%
ValueCountFrequency (%)
0 1065
12.2%
1 1121
12.8%
2 1109
12.7%
3 1143
13.0%
4 1077
12.3%
5 1079
12.3%
6 1074
12.3%
7 1095
12.5%
ValueCountFrequency (%)
7 1095
12.5%
6 1074
12.3%
5 1079
12.3%
4 1077
12.3%
3 1143
13.0%
2 1109
12.7%
1 1121
12.8%
0 1065
12.2%

Sleep-Hours-Per-Day
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0235079
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-10-10T16:18:55.718269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q15
median7
Q39
95-th percentile10
Maximum10
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9884728
Coefficient of variation (CV)0.28311675
Kurtosis-1.2323539
Mean7.0235079
Median Absolute Deviation (MAD)2
Skewness0.00035685175
Sum61547
Variance3.9540239
MonotonicityNot monotonic
2023-10-10T16:18:55.931938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
10 1293
14.8%
8 1288
14.7%
6 1276
14.6%
7 1270
14.5%
5 1263
14.4%
9 1192
13.6%
4 1181
13.5%
ValueCountFrequency (%)
4 1181
13.5%
5 1263
14.4%
6 1276
14.6%
7 1270
14.5%
8 1288
14.7%
9 1192
13.6%
10 1293
14.8%
ValueCountFrequency (%)
10 1293
14.8%
9 1192
13.6%
8 1288
14.7%
7 1270
14.5%
6 1276
14.6%
5 1263
14.4%
4 1181
13.5%

Country
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
Germany
 
477
Argentina
 
471
Brazil
 
462
United Kingdom
 
457
Australia
 
449
Other values (15)
6447 

Length

Max length14
Median length12
Mean length7.9454525
Min length5

Characters and Unicode

Total characters69626
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArgentina
2nd rowCanada
3rd rowFrance
4th rowCanada
5th rowThailand

Common Values

ValueCountFrequency (%)
Germany 477
 
5.4%
Argentina 471
 
5.4%
Brazil 462
 
5.3%
United Kingdom 457
 
5.2%
Australia 449
 
5.1%
Nigeria 448
 
5.1%
France 446
 
5.1%
Canada 440
 
5.0%
China 436
 
5.0%
New Zealand 435
 
5.0%
Other values (10) 4242
48.4%

Length

2023-10-10T16:18:56.192791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united 877
 
8.0%
south 834
 
7.6%
germany 477
 
4.4%
argentina 471
 
4.3%
brazil 462
 
4.2%
kingdom 457
 
4.2%
australia 449
 
4.1%
nigeria 448
 
4.1%
france 446
 
4.1%
canada 440
 
4.0%
Other values (13) 5548
50.9%

Most occurring characters

ValueCountFrequency (%)
a 10931
15.7%
n 6638
 
9.5%
i 6597
 
9.5%
e 4843
 
7.0%
t 4327
 
6.2%
r 3587
 
5.2%
d 3049
 
4.4%
l 2634
 
3.8%
o 2558
 
3.7%
2146
 
3.1%
Other values (26) 22316
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56571
81.2%
Uppercase Letter 10909
 
15.7%
Space Separator 2146
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10931
19.3%
n 6638
11.7%
i 6597
11.7%
e 4843
8.6%
t 4327
 
7.6%
r 3587
 
6.3%
d 3049
 
5.4%
l 2634
 
4.7%
o 2558
 
4.5%
m 1788
 
3.2%
Other values (11) 9619
17.0%
Uppercase Letter
ValueCountFrequency (%)
S 1684
15.4%
A 1345
12.3%
C 1305
12.0%
N 883
8.1%
U 877
8.0%
K 866
7.9%
I 843
7.7%
G 477
 
4.4%
B 462
 
4.2%
F 446
 
4.1%
Other values (4) 1721
15.8%
Space Separator
ValueCountFrequency (%)
2146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 67480
96.9%
Common 2146
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10931
16.2%
n 6638
 
9.8%
i 6597
 
9.8%
e 4843
 
7.2%
t 4327
 
6.4%
r 3587
 
5.3%
d 3049
 
4.5%
l 2634
 
3.9%
o 2558
 
3.8%
m 1788
 
2.6%
Other values (25) 20528
30.4%
Common
ValueCountFrequency (%)
2146
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69626
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10931
15.7%
n 6638
 
9.5%
i 6597
 
9.5%
e 4843
 
7.0%
t 4327
 
6.2%
r 3587
 
5.2%
d 3049
 
4.4%
l 2634
 
3.8%
o 2558
 
3.7%
2146
 
3.1%
Other values (26) 22316
32.1%

Continent
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
Asia
2543 
Europe
2241 
South America
1362 
Australia
884 
Africa
873 

Length

Max length13
Median length9
Mean length7.4972042
Min length4

Characters and Unicode

Total characters65698
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth America
2nd rowNorth America
3rd rowEurope
4th rowNorth America
5th rowAsia

Common Values

ValueCountFrequency (%)
Asia 2543
29.0%
Europe 2241
25.6%
South America 1362
15.5%
Australia 884
 
10.1%
Africa 873
 
10.0%
North America 860
 
9.8%

Length

2023-10-10T16:18:56.450620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T16:18:56.761214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
asia 2543
23.1%
europe 2241
20.4%
america 2222
20.2%
south 1362
12.4%
australia 884
 
8.0%
africa 873
 
7.9%
north 860
 
7.8%

Most occurring characters

ValueCountFrequency (%)
a 7406
11.3%
r 7080
10.8%
A 6522
9.9%
i 6522
9.9%
u 4487
 
6.8%
o 4463
 
6.8%
e 4463
 
6.8%
s 3427
 
5.2%
t 3106
 
4.7%
c 3095
 
4.7%
Other values (9) 15127
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 52491
79.9%
Uppercase Letter 10985
 
16.7%
Space Separator 2222
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7406
14.1%
r 7080
13.5%
i 6522
12.4%
u 4487
8.5%
o 4463
8.5%
e 4463
8.5%
s 3427
6.5%
t 3106
5.9%
c 3095
5.9%
p 2241
 
4.3%
Other values (4) 6201
11.8%
Uppercase Letter
ValueCountFrequency (%)
A 6522
59.4%
E 2241
 
20.4%
S 1362
 
12.4%
N 860
 
7.8%
Space Separator
ValueCountFrequency (%)
2222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63476
96.6%
Common 2222
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7406
11.7%
r 7080
11.2%
A 6522
10.3%
i 6522
10.3%
u 4487
 
7.1%
o 4463
 
7.0%
e 4463
 
7.0%
s 3427
 
5.4%
t 3106
 
4.9%
c 3095
 
4.9%
Other values (8) 12905
20.3%
Common
ValueCountFrequency (%)
2222
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65698
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7406
11.3%
r 7080
10.8%
A 6522
9.9%
i 6522
9.9%
u 4487
 
6.8%
o 4463
 
6.8%
e 4463
 
6.8%
s 3427
 
5.2%
t 3106
 
4.7%
c 3095
 
4.7%
Other values (9) 15127
23.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
No
5624 
Si
3139 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters35052
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row No
2nd row No
3rd row No
4th row No
5th row No

Common Values

ValueCountFrequency (%)
No 5624
64.2%
Si 3139
35.8%

Length

2023-10-10T16:18:57.049781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-10T16:18:57.322095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 5624
64.2%
si 3139
35.8%

Most occurring characters

ValueCountFrequency (%)
17526
50.0%
N 5624
 
16.0%
o 5624
 
16.0%
S 3139
 
9.0%
i 3139
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator 17526
50.0%
Uppercase Letter 8763
25.0%
Lowercase Letter 8763
25.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 5624
64.2%
S 3139
35.8%
Lowercase Letter
ValueCountFrequency (%)
o 5624
64.2%
i 3139
35.8%
Space Separator
ValueCountFrequency (%)
17526
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17526
50.0%
Latin 17526
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 5624
32.1%
o 5624
32.1%
S 3139
17.9%
i 3139
17.9%
Common
ValueCountFrequency (%)
17526
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17526
50.0%
N 5624
 
16.0%
o 5624
 
16.0%
S 3139
 
9.0%
i 3139
 
9.0%

Interactions

2023-10-10T16:18:33.727930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:16.647860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:18.813652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:21.188014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:24.568732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:27.217343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:29.346675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:31.516221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:34.284948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:16.934917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:19.118137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:21.603242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:25.439688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:27.472798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:29.612695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:31.797895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:34.531997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:17.188026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:19.366764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:21.970162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:25.684939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:27.721279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:29.857503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:32.063884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:34.788361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:17.451344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:19.630018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:22.394607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:25.943525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:27.983591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:30.134480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:32.367496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:35.018562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:17.715496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:19.905664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:22.782554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:26.178092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:28.235980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:30.384522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:32.637225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:35.259392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:17.974310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:20.168233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:23.198117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:26.427182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:28.490005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:30.656124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:32.910899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:35.551241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:18.258164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:20.463715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:23.665344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:26.698481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:28.784671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:30.938073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:33.196507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:35.966161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:18.529441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:20.790653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:24.127335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:26.947593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:29.060367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:31.220763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-10T16:18:33.473839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-10T16:18:57.538706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AgeCholesterolHeart-RateStress-LevelIncomeTriglyceridesPhysical-Activity-Days-Per-WeekSleep-Hours-Per-DaySexDiabetesFamily-HistorySmokingObesityAlcohol-ConsumptionDietPrevious-Heart-ProblemsMedication-UseCountryContinentHeart-Attack-Risk
Age1.000-0.009-0.0040.018-0.0020.0040.001-0.0020.0150.0000.0000.4760.0000.0000.0110.0000.0000.0120.0020.012
Cholesterol-0.0091.0000.000-0.024-0.000-0.0060.0160.0040.0040.0210.0000.0120.0000.0230.0000.0000.0000.0000.0000.000
Heart-Rate-0.0040.0001.000-0.0050.0050.0120.0010.0020.0000.0000.0000.0000.0140.0000.0170.0000.0000.0150.0000.000
Stress-Level0.018-0.024-0.0051.000-0.003-0.0040.007-0.0140.0160.0000.0170.0000.0260.0000.0120.0000.0390.0080.0220.000
Income-0.002-0.0000.005-0.0031.0000.0110.000-0.0070.0000.0000.0250.0000.0000.0170.0000.0000.0000.0120.0000.017
Triglycerides0.004-0.0060.012-0.0040.0111.000-0.008-0.0290.0280.0200.0000.0200.0000.0160.0190.0000.0000.0000.0000.000
Physical-Activity-Days-Per-Week0.0010.0160.0010.0070.000-0.0081.0000.0140.0000.0180.0250.0000.0000.0160.0000.0150.0140.0000.0000.011
Sleep-Hours-Per-Day-0.0020.0040.002-0.014-0.007-0.0290.0141.0000.0100.0300.0200.0100.0000.0000.0120.0000.0240.0000.0130.008
Sex0.0150.0040.0000.0160.0000.0280.0000.0101.0000.0000.0000.5140.0000.0000.0000.0000.0000.0000.0030.000
Diabetes0.0000.0210.0000.0000.0000.0200.0180.0300.0001.0000.0080.0000.0070.0000.0000.0000.0000.0000.0000.013
Family-History0.0000.0000.0000.0170.0250.0000.0250.0200.0000.0081.0000.0040.0000.0060.0000.0000.0000.0000.0150.000
Smoking0.4760.0120.0000.0000.0000.0200.0000.0100.5140.0000.0041.0000.0000.0060.0000.0000.0000.0000.0000.000
Obesity0.0000.0000.0140.0260.0000.0000.0000.0000.0000.0070.0000.0001.0000.0210.0000.0000.0000.0000.0000.008
Alcohol-Consumption0.0000.0230.0000.0000.0170.0160.0160.0000.0000.0000.0060.0060.0211.0000.0000.0000.0000.0000.0000.008
Diet0.0110.0000.0170.0120.0000.0190.0000.0120.0000.0000.0000.0000.0000.0001.0000.0130.0160.0210.0160.000
Previous-Heart-Problems0.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0131.0000.0000.0340.0250.000
Medication-Use0.0000.0000.0000.0390.0000.0000.0140.0240.0000.0000.0000.0000.0000.0000.0160.0001.0000.0030.0000.000
Country0.0120.0000.0150.0080.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0340.0031.0000.9990.012
Continent0.0020.0000.0000.0220.0000.0000.0000.0130.0030.0000.0150.0000.0000.0000.0160.0250.0000.9991.0000.000
Heart-Attack-Risk0.0120.0000.0000.0000.0170.0000.0110.0080.0000.0130.0000.0000.0080.0080.0000.0000.0000.0120.0001.000

Missing values

2023-10-10T16:18:36.666701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-10T16:18:37.969987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Patient-IDAgeSexCholesterolBlood-PressureHeart-RateDiabetesFamily-HistorySmokingObesityAlcohol-ConsumptionExercise-Hours-Per-WeekDietPrevious-Heart-ProblemsMedication-UseStress-LevelSedentary-Hours-Per-DayIncomeBMITriglyceridesPhysical-Activity-Days-Per-WeekSleep-Hours-Per-DayCountryContinentHeart-Attack-Risk
0BMW781267Masculino208158/8872NegativoNegativoSiNoNo4.168.188.835.442.070PromedioNegativoNo966.150.014.529.140.50026140431.251.232.725.295.40028606ArgentinaSouth AmericaNo
1CZE111421Masculino389165/9398PositivoPositivoSiSiSi18.132.416.178.634.400EnfermizoPositivoNo14.963.458.839.757.670285768271.949.733.519.87423517CanadaNorth AmericaNo
2BNI990621Femenino324174/9972PositivoNegativoNoNoNo20.783.529.861.178.800SanoPositivoSi99.463.425.838.029.82023528228.176.570.683.909.80058744FranceEuropeNo
3JLN349784Masculino383163/10073PositivoPositivoSiNoSi982.812.959.348.533PromedioPositivoNo97.648.980.824.461.00012564036.464.704.293.082.80037834CanadaNorth AmericaNo
4GFO884766Masculino31891/8893PositivoPositivoSiSiNo5.804.298.820.315.430EnfermizoPositivoNo615.148.209.264.291.30016055521.809.144.180.619.70023115ThailandAsiaNo
5ZOO794154Femenino297172/8648PositivoPositivoSiNoSi6.250.080.237.057.350EnfermizoPositivoSi27.798.752.408.582.43024133920.146.839.503.010.000795510GermanyEuropeSi
6WYV096690Masculino358102/7384NegativoNegativoSiNoSi4.098.177.090.985.470SanoNegativoNo76.273.560.009.569.51019045028.885.810.606.590.400284410CanadaNorth AmericaSi
7XXM097284Masculino220131/68107NegativoNegativoSiSiSi3.427.928.754.300.870PromedioNegativoSi410.543.780.239.266.800122093222.218.617.394.03837067JapanAsiaSi
8XCQ593720Masculino145144/10568PositivoNegativoSiSiNo16.868.302.239.450.000PromedioNegativoNo511.348.786.873.498.900250863.580.990.131.909.64079074BrazilSouth AmericaNo
9FTJ545643Femenino248160/7055NegativoPositivoSiSiSi1.945.150.606.299.490EnfermizoNegativoNo44.055.114.781.794.6002097032.255.891.675.229.81023277JapanAsiaNo
Patient-IDAgeSexCholesterolBlood-PressureHeart-RateDiabetesFamily-HistorySmokingObesityAlcohol-ConsumptionExercise-Hours-Per-WeekDietPrevious-Heart-ProblemsMedication-UseStress-LevelSedentary-Hours-Per-DayIncomeBMITriglyceridesPhysical-Activity-Days-Per-WeekSleep-Hours-Per-DayCountryContinentHeart-Attack-Risk
8753NVC870482Masculino311126/10887NegativoPositivoSiSiSi8.202.448.587.890.530EnfermizoPositivoNo78.402.977.140.087.2101415212.769.423.979.513.46051515AustraliaAustraliaSi
8754LZM360680Femenino383153/9691PositivoPositivoSiNoSi6.082.655.613.500.490SanoPositivoSi38.234.883.024.427.17010134120.490.450.321.507.00017434ArgentinaSouth AmericaSi
8755KQR894925Masculino307137/9478NegativoPositivoSiNoSi32.722.021.401.897.500PromedioNegativoNo310.516.774.509.379.2007921133.469.360.438.108.90029675SpainEuropeNo
8756BUE041622Masculino347115/87108NegativoPositivoSiNoSi3.820.772.242.810.590SanoPositivoSi16.786.750.468.117.3202302352.029.505.383.508.78064129CanadaNorth AmericaSi
8757YDX247859Femenino37893/7899NegativoPositivoSiSiNo1.857.908.161.254.340SanoNegativoNo47.495.230.555.888.59070415399.760.614.212.37915819ChinaAsiaNo
8758MSV991860Masculino12194/7661PositivoPositivoSiNoSi7.917.341.801.057.810SanoPositivoSi810.806.373.209.059.8002354201.965.589.452.538.6906777ThailandAsiaNo
8759QSV676428Femenino120157/10273PositivoNegativoNoSiNo165.584.261.756.953SanoNegativoNo838.330.380.556.468.00021788123.993.866.102.650.50061749CanadaNorth AmericaNo
8760XKA592547Masculino250161/75105NegativoPositivoSiSiSi31.484.379.076.856.000PromedioPositivoNo523.752.137.292.172.000369983.540.614.615.890.45052744BrazilSouth AmericaSi
8761EPE680136Masculino178119/6760PositivoNegativoSiNoNo37.899.498.336.153.700EnfermizoPositivoSi52.910.425.818.218.5602099432.729.402.008.767.08011428BrazilSouth AmericaNo
8762ZWN966625Femenino356138/6775PositivoPositivoNoNoSi18.081.747.965.841.100SanoNegativoNo89.005.234.382.727.1102473383.291.415.085.636.53018074United KingdomEuropeSi